Ever felt like you’re throwing marketing dollars into a black hole? You’re running campaigns, tracking clicks, and conversions, but a nagging question remains: are these efforts truly driving growth, or would these sales have happened anyway? This is where the idea of incrementality in marketing comes into sharp focus, offering a way to measure the true impact of your efforts.
Incrementality in marketing is about figuring out the actual lift in sales caused by specific marketing actions. It’s about proving that your strategies are leading to results that matter.
Table Of Contents:
- What Exactly is Incrementality in Marketing?
- The Importance of Measuring Incrementality
- Methods for Measuring Incrementality
- Practical Applications of Incrementality in Marketing Measurement
- Challenges and Considerations
- Tools and Technologies for Incrementality Testing
- Conclusion
What Exactly is Incrementality in Marketing?
Think of it like this: imagine you see an ad for a product you were already thinking of buying. Did that ad prompt the purchase, or were you already planning it?
Incrementality in marketing focuses on cause and effect in the marketing context. This will provide for us a framework for more effective spending and budget optimization.
It helps us measure actions to see how they improve results, such as installs or actions related to buying something in-app, as Adjust notes. We then get an understanding for if those actions were triggered by the marketing actions or whether they might have happened organically.
Defining Incremental Lift
Incremental lift is the actual boost a campaign gives. Think of those times where a clever ad or promotion seemed to reel in more sales, not just a change to the customers we were attracting.
We’re really aiming for an improvement beyond what our typical sales numbers show—that’s lift. So, how does the “incremental Return on Ad Spend (iROAS)” play out?
iROAS – Measuring True Return
iROAS helps pinpoint revenue increase due to ad efforts, separating ad-driven gains from organic behavior. With solutions like Adjust’s InSight, campaigns can be carefully tracked.
Clients gain a clear, privacy-centric perspective on how new campaigns really work.
The Importance of Measuring Incrementality
Knowing the true impact of your marketing spend is a game-changer, especially with privacy concerns growing and user data becoming harder to access. Following customer actions is becoming harder because of privacy rules like GDPR.
This makes incrementality testing key for understanding digital advertising success. Traditional metrics fall short; for instance, relying on the last-touch attribution model can be misleading.
It is like crediting a movie’s success solely to the poster outside the theater, ignoring all other promotional efforts.
Incrementality vs. Attribution
Attribution models try to credit each marketing touchpoint that leads to a conversion. But, incrementality goes further by determining which of those touchpoints made the *difference*.
Often, we find that sales get credited as influenced when really customers would have come in organically anyway. So, in reality, attribution, as Adjust mentions, helps find the link, maybe from a click on your campaign, but it is going to fall short by itself.
We are working on determining what we really have in a true customer. Customers now often check products on multiple channels before deciding, so knowing which move really sealed the deal is super important—not necessarily the total sales number.
Why Traditional Metrics Fall Short
Common metrics like Return on Ad Spend (ROAS) give a limited view. We only understand ad spend impact and not the fuller picture, such as channel synergies and cross-channel effects.
The reason is simple: if 10,000 installs happened, but only 2,000 were driven by the campaign we were running, what is truly incremental? Incrementality measurement provides a way to check campaigns driven by sales lift, in practice.
It stands out as essential in evaluating value because other tracking methods sometimes struggle due to data and privacy complications. This helps advertisers get a clearer picture. Metrics like ROAS just show costs and attributed revenue, but this alone won’t tell you if that marketing really made the difference; to understand impact, you get help with incrementality measurements.
Methods for Measuring Incrementality
There are a couple of main methods we might choose to test the idea, by comparing behavior.
- Causal Inference
- A/B Testing
- Budget Holdout
- Geo-Lifting
Traditionally measuring incrementality can be done in different ways, including an approach where we hold part of our money back, carefully monitoring things. However, you will come to find the most robust of options is through a nuanced view with Causal Inference.
Holdout Experiments
This method involves removing a marketing channel from a segment of your audience and observing their behavior compared to a control group. As Measured highlights, this helps isolate the impact of a specific channel.
Say we stopped marketing something for a while and then we compare it to how other users react, using things like PSA, Ghost ads, and so forth. The goal is to check the change without specific exposure.
One of the drawbacks here is how complex and costly it might be to test across lots of markets at the same time. Sometimes, it’s hard to draw concrete answers, and we may have a high opportunity cost while waiting.
Scale Experiments
This approach is pretty much the opposite of a holdout experiment. We amplify channel investments up for a chosen group, typically by two to four times the norm.
Then, the activity we observe, maybe visits or cart adds, is then measured against a control group. It can reveal how budget changes are working for you.
A risk is when your results could become less clear the more channels and marketing things we have overlapping in any test.
Multi-Treatment Experiments
This strategy can be costly and complex to execute. However, when used successfully, the results have been very helpful.
Multiple channels get tested to measure effectiveness. We divide audiences and apply distinct mixes of the selected marketing channels to groups for analysis.
Comparing conversion behavior of exposed groups against unexposed controls provides accurate data to understand campaign value. Consider splitting an audience into multiple cells for comparison, then seeing a few treatments; each would react in the way the experiment wants, and this comparison, set against usual traffic, tells what works alone and what functions great together.
Audience Split Types: Known-Audience vs. Geographical
When testing, it’s going to come down to understanding if the marketing caused sales or actions from new customers. You’ve got to measure if it moved users from “we have a passing thought about doing business” to the sale, purchase, or sign-up.
Known audience split tests only work if individual users can be tagged. Otherwise, a known-audience split won’t do for platforms such as broad social sites.
Geographical splits use statistics by identifying specific markets within regions for experiment accuracy and results. Incrementality experiments rely on 1st-party data; as Measured.com notes, this contrasts with on-platform lift tests. It covers the net impact of various marketing touches on a product or service sales by an entity, and external tests don’t see these chain effects.
Practical Applications of Incrementality in Marketing Measurement
Once you understand incrementality, you can make better investment decisions, optimizing budget allocation. A marketer should always have the ability to pivot and reallocate dollars to top-performers for improved ROI, and other efficiencies.
How might it work to analyze ad performance by the numbers?
Case Study: Soft Surroundings
A notable example, shared by Measured.com, involved Soft Surroundings, a women’s clothing retailer. After conducting retargeting experiments, they cut spending substantially.
They then allocated more dollars towards prospecting tactics, such as on Facebook. This improved revenue considerably year-over-year.
Using Incrementality for Budget Optimization
You can aim to understand how you might maximize sales, minimize your spend, or grow the dollars needed to maintain that return on investment.
But what should we expect to see over time? We really need to have marketing be able to pivot in the advertising space; for more information on incrementality measurement, we need to remember, as Incrmntal mentions, INCRMNTAL does not require user-level data to achieve optimization results.
Challenges and Considerations
Even though testing incrementality provides deep information, hurdles can surface.
Collinearity Challenges
Often, a brand tends to ramp things up, like marketing spends and advertising strategies. The inherent interconnection might cause the outcomes from marketing and business improvements to be mixed and overlapping when looking back on performance improvements.
As a way to explain it better, we might show that a brand saw ad spending increase by one percent in one test market. The return they would receive on advertising spend would go down due to this “Adstock effect”.
Another consideration we need to consider? Lower funnel strategies where you’re engaging with branded searches, such as when potential clients already look at a website before retargeting; incrementality analysis can get difficult if there is more demand in lower funnel, but there are experiments to help show actual sales from channels when correctly deployed.
Tools and Technologies for Incrementality Testing
Choosing the right software really helps you make better decision-making for understanding incremental measurement campaigns.
Platforms often are offering advanced measurement methods that factor in causal inferences. This will offer detailed comparison data across all campaign efforts, rather than siloed information on platforms.
Platforms for Measuring Incrementality in Marketing
Solutions can vary between simple ones for testing, like A/B, to more comprehensive toolboxes. The main goal when choosing an incrementality testing platform is the measurement and reporting process needs to become a data-backed, informed discussion across an organization.
Data scientists need ways of exploring data as they build, test, validate, and deploy.
| Platform | Key Features | Ideal Use Case |
|---|---|---|
| Measured | Cross-channel incrementality testing, detailed reporting, integration with various data sources | Brands looking for a comprehensive measurement framework that offers real-time insights |
| Adjust | AI-driven incrementality measurement with a 95% confidence interval, industry benchmark leveraging, actionable recommendations | App marketers wanting precise, privacy-compliant testing, campaign cannibalization insights |
| INCRMNTAL | Measurement without A/B tests, utilizes causal discovery and ensemble time-series models, real-time insights | Marketers needing to avoid A/B testing complexities and wanting ongoing adaptive measurement |
Automated Solutions
Many tasks are repetitive with manual processes.
Solutions exist that simplify analysis to support various activities. Think about testing, automating actions, and real-time insights that free people to analyze and improve strategy with the data now readily available; they automate key measurement processes and boost decision-making with less manual interference and data collection.
Conclusion
Incrementality in marketing shows just how crucial it is to distinguish actual sales lift from mere chance in our activities. Embracing this methodology lets brands focus marketing efforts.
It boosts sales by correctly demonstrating what works best. It also helps ensure that spending aligns with clear growth goals.
Moving forward in digital strategies, embracing incrementality in marketing insights on actual impact guides better decision-making to align investments.
